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Street view number detection is called
natural scene text recognition problem which is quite different from printed
character or handwritten recognition. Research in this field was started in
90’s, but still it is considered as an unsolved issue. As I mentioned earlier
that the difficulties arise due to fonts variation, scales, rotations, low
lights etc.

     In earlier years to deal with natural
scene text identification sequentially, first character classification by
sliding window or connected components mainly used. 4 After that word
prediction can be done by predicting character classifier in left to right
manner. Recently segmentation method guided by supervised classifier use where
words can be recognized through a sequential beam search. 4 But none of this
can help to solve the street view recognition problem.

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     In recent works convolutional neural
networks proves its capabilities more accurately to solve object recognition
task. 4 Some research has done with CNN to tackle scene text recognition
tasks. 4 Studies on CNN shows its huge capability to represent all types of
character variation in the natural scene and till now it is holding this high
variability. Analysis with convolutional neural network stars at early 80’s and
it successfully applied for handwritten digit recognition in 90’s. 4 With the
recent development of computer resources, training sets, advance algorithm and
dropout training deep convolutional neural networks become more efficient to recognize
natural scene digit and characters. 3

      Previously CNN used mainly to detecting a
single object from an input image. It was quite difficult to isolate each
character from a single image and identify them. Goodfellow et al., solve this
problem by using deep large CNN directly to model the whole image and with a
simple graphical model as the top inference layer. 4

      The rest of the paper is designed in
section III Convolutional neural network architecture, section IV Experiment,
Result, and Discussion and Future Work and Conclusion in section V.

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